[visionlist] 2019 AMFG at CVPR: Last Call for Papers

[Joseph Robinson]

[image: CVPRLogo.jpg]
The 9th IEEE Internation Workshop on
Analysis and Modeling of Faces and Gestures (AMFG2019)
<https://web.northeastern.edu/smilelab/amfg2019/>




(Apologies for multiple postings)
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* 2019 AMFG Workshop
                                 *
*IEEE Conference on Computer Vision and Pattern Recognition
              *
*Submission Deadline: 5 March 2019   *
Submission Site:
https://cmt3.research.microsoft.com/AMFG2019/Submission/Index

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This is a last call for the CVPR <http://cvpr2019.thecvf.com/> Workshop on 2019
AMFG <https://web.northeastern.edu/smilelab/amfg2019/>, the 9th edition.


*Call for Papers*

We have experienced rapid advances in the face, gesture, and cross-modality
(e.g., voice and face) technologies. This is due with many thanks to the
deep learning (i.e., dating back to 2012, AlexNet) and large-scale, labeled
image collections. The progress made in deep learning continues to push
renown public databases to near saturation which, thus, calls more evermore
challenging image collections to be compiled as databases. In practice, and
even widely in applied research, using off-the-shelf deep learning models
has become the norm, as numerous pre-trained networks are available for
download and are readily deployed to new, unseen data (e.g., VGG-Face,
ResNet, amongst other types). We have almost grown “spoiled” from such
luxury, which, in all actuality, has enabled us to stay hidden from many
truths. Theoretically, the truth behind what makes neural networks more
discriminant than ever before is still, in all fairness, unclear—rather,
they act as a sort of black box to most practitioners and even researchers,
alike. More troublesome is the absence of tools to quantitatively and
qualitatively characterize existing deep models, which, in itself, could
yield greater insights about these all so familiar black boxes. With the
frontier moving forward at rates incomparable to any spurt of the past,
challenges such as high variations in illuminations, pose, age, etc., now
confront us. However, state-of-the-art deep learning models often fail when
faced with such challenges owed to the difficulties in modeling structured
data and visual dynamics.

Alongside the effort spent on conventional face recognition is the research
is done across modality learning, such as face and voice, gestures in
imagery and motion in videos, along with several other tasks. This line of
work has attracted attention from industry and academic researchers from
all sorts of domains. Additionally, and in some cases with this, there has
been a push to advance these technologies for social media based
applications. Regardless of the exact domain and purpose, the following
capabilities must be satisfied: face and body tracking (e.g., facial
expression analysis, face detection, gesture recognition), lip reading and
voice understanding, face and body characterization (e.g., behavioral
understanding, emotion recognition), face, body and gesture characteristic
analysis (e.g., gait, age, gender, ethnicity recognition), group
understanding via social cues (e.g., kinship, non-blood relationships,
personality), and visual sentiment analysis (e.g., temperament,
arrangement). Thus, needing to be able to create effective models for
visual certainty has significant value in both the scientific communities
and the commercial market, with applications that span topics of
human-computer interaction, social media analytics, video indexing, visual
surveillance, and internet vision. Currently, researchers have made
significant progress addressing the many of these problems, and especially
when considering off-the-shelf and cost-efficient vision HW products
available these days, e.g. Intel RealSense, Magic Leap, SHORE, and Affdex.
Nonetheless, serious challenges still remain, which only amplifies when
considering the unconstrained imaging conditions captured by different
sources focused on non-cooperative subjects. It is these latter challenges
that especially grabs our interest, as we sought out to bring together the
cutting-edge techniques and recent advances of deep learning to solve the
challenges in the wild.

This one-day serial workshop (i.e., AMFG2019) provides a forum for
researchers to review the recent progress of recognition, analysis, and
modeling of face, body, and gesture, while embracing the most advanced deep
learning systems available for face and gesture analysis, particularly,
under an unconstrained environment like social media and across modalities
like face to voice. The workshop includes up to 3 keynotes and
peer-reviewed papers (oral and poster). Original high-quality contributions
are solicited on the following topics:


   - Deep learning methodology, theory, as applied to social media
   analytics;
   - Data-driven or physics-based generative models for faces, poses, and
   gestures; Deep learning for internet-scale soft biometrics and profiling:
   age, gender, ethnicity, personality, kinship, occupation, beauty ranking,
   and fashion classification by facial or body descriptor;
   - Novel deep model, deep learning survey, or comparative study for
   face/gesture recognition;
   - Deep learning for detection and recognition of faces and bodies with
   large 3D rotation, illumination change, partial occlusion, unknown/changing
   background, and aging (i.e., in the wild); especially large 3D rotation
   robust face and gesture recognition;
   - Motion analysis, tracking, and extraction of face and body models
   captured from several non-overlapping views;
   - Face, gait, and action recognition in low-quality (e.g., blurred), or
   low-resolution video from fixed or mobile device cameras;
   - AutoML for face and gesture analysis;
   - Mathematical models and algorithms, sensors and modalities for face &
   body gesture and action representation, analysis, and recognition for
   cross-domain social media;
   - Social/psychological based studies that aids in understanding
   computational modeling and building better-automated face and gesture
   systems with interactive features;
   - Multimedia learning models involving faces and gestures (e.g., voice,
   wearable IMUs, and face);
   - Social applications involving detection, tracking & recognition of
   face, body, and action;
   - Face and gesture analysis for sentiment analysis in the social context;
   - Other applications involving face and gesture analysis in social media
   content.


Sincerely,

Joe

*Joseph P Robinson *
Ph.D. Candidate-- SMILE Lab <https://web.northeastern.edu/smilelab/>

Northeastern University
Email: jrobins1 at coe.neu.edu
Website: www.jrobsvision.com
Cell: (978) 918-2701




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